Technique for lane assignment in a vehicle

ABSTRACT

A technique for assigning lanes on a road to objects moving in a vicinity of a vehicle on the road is proposed. A method embodiment of the invention comprises the steps of providing trajectories, wherein the or each trajectory represents a time sequence of positions of a moving object; selecting first and second objects and determining a distance between a current position of the first object and the trajectory of the second object; comparing the distance with a predefined threshold; and providing, based on a result of the comparison, a lane assignment indicating a lane to which the second object is assigned.

The present invention relates to techniques for assisting a driver in avehicle, and in particular to a method and system for assigning lanes ona road to objects moving in a vicinity of the vehicle.

A manifold of driving assistance systems is available today which aim atincreasing comfort and/or safety of the passengers of a vehicle. Systemsrelated to driving or manoeuvring are based on sensing equipment such asshort range active systems including ultrasonic systems, long rangeactive systems including radar, lidar, or infra-red, passive systemssuch as cameras, and/or may make use of Car2X communication includingvehicle-to-vehicle communication and/or communication between vehicleand roadside units, which may be based on mobile network communicationor other wireless network communication systems such as Wireless LAN.

Functions related to driving or manoeuvring range from distance sensingand passive or active parking assistance to sophisticated “AdvancedDriver Assistant Systems” (ADAS) such as, for example, cruise-controlfunctions, e.g. “Intelligent Adaptive Cruise Control” (IACC), which mayinclude a detection of other vehicles or objects moving in front orbehind the ego-vehicle, and may include even functions for predicting afuture behaviour of neighboring objects, e.g. lane change. ADASfunctions may also relate to collision mitigation, emergency braking,etc., or may include lane change assistants, which need to be able atfirst instance to determine whether there is more than one laneavailable and if so, how many.

Many ADAS functions rely on assigning previously detected objects orroad users to one of multiple lanes. A correct assignment of objects tolanes is crucial for systems such as cruise-control, collisionavoidance/mitigation or emergency breaking, as the operation depends onthe detected object's lane, for example whether or not it is theego-lane.

Conventional approaches for lane assignment may use lane positioninformation for assigning vehicles to different lanes. A lane assignmentis preceded by a lane marking detection using, e.g., camera or otherimage data. The lane marking lines are extracted from the image data.Then the positions of other road users as detected by, e.g., radar ismapped to the image domain and compared with the extracted lane markingpositions in order to assign the lanes to the users.

More specifically, the road course is detected and the lane offset iscalculated dependent on the current position of the road user and the“position” of the road at the distance of the road user. This approachrequires a separate detection or determination of (1) lane positioninformation and (2) position information of the road user/s. The laneposition information may be gathered from a camera providing image data,which is then to be analyzed for identifying a lane or lanes, forexample by detecting lane marking lines in white and/or other colors.The object position/s may be determined independently by radar, lidar,and/or Car2X. A processing of the data may then comprise mapping objectpositions to road positions/lanes, which includes determining a roadposition at an object position. Eventually, a lane assignment of objectsto lanes may be provided to other functions of the vehicle assistantand/or output to the driver.

EP 1 193 661 A1 describes a lane recognition apparatus for a vehicle,which is based on camera images.

DE 197 49 086 C1 describes an apparatus for determining data indicatingtraffic lane evolution. Information of a camera-based lane recognitionsystem is combined with radar-based position data related to other roadusers ahead of the ego-vehicle. The course of the road is estimated.Then the position of the other vehicles is determined with respect tothe ego-lane, i.e. the lane of the ego-vehicle.

The above-described systems are expensive, as they require varioussensor systems, for example at least one camera for road detection andat least one radar for object detection. For cost-limiting reasons, thedata processing should be robust enough to be based on a simple cameraor simple radar system. However, a camera-based road course/laneestimation turns out to be error-prone because of, for example, badvisibility conditions due to fog, rain, darkness, etc. Radar detectionaccuracy is limited by noise in a lateral position estimation, forexample, and the various error sources add together such that anavailable precision is limited. It is also to be noted that theapplicability of the methods is normally limited by the limited camerarange which may be considerably smaller than an object detection range.Therefore there is a general need to improve on these methods of laneassignment.

Meis et al., “A New Method for Robust Far-distance Road CourseEstimation in Advanced Driver Assistance Systems”, 13th InternationalIEEE Conference on Intelligent Transportation Systems (ITSC), 19-22 Sep.2010, Funchal, p. 1357-1362, ISBN: 978-1-4244-7657-2, describe animprovement on lane detection by means of combining video data and radardata using a Kalman filter. From the radar data static targets at theroad boundary are extracted (e.g. bushes, crash barriers, road posts).Fusing video and radar data enables a higher precision in particular inpoor visibility conditions due to, e.g., fog.

Other known approaches of lane assignment use only radar data to assignroad users to the ego-lane, i.e. on the same lane as the ego-vehicle.Such systems uses available radar data such as positions and velocitiesof detected objects to determine static targets and then estimate a roadcourse based on the determined static targets. An at least simple lanemodel is required that may, for example, be derived from the steeringangle of the ego-vehicle. However, such simple model this is prone tofail especially when the road changes from curved to straight. Moresophisticated models may require map data to be available fordetermining a number of lanes.

Adam, C. et al., “Probabilistic Road Estimation and Lane AssociationUsing Radar Detections”, Proceedings of the 14th InternationalConference on Information Fusion, 5-8 Jul. 2011, Chicago, Ill., USA, p.1-8, ISBN: 978-1-4577-0267-9, describe a probabilistic way of estimatingthe road course and a lane association using only radar information.First, the static targets are extracted to estimate the road boundaryand its shape. Afterwards, the dynamic obstacles on the road areanalyzed in order to assign each to a lane. For doing so map data isrequired in order to get the number of existing lanes. Then for eachroad user a probability for each lane is calculated. This probabilityindicates how likely it is that a road user is on a particular lane. Forcalculating the probabilities information such as position, velocity,heading direction, yaw rate from the radar is used.

DE 10 2010 023 196 A1 discloses a technique in the field of AdaptiveCruise Control (ACC). The method is directed to detecting a predecessorvehicle on the ego-lane by means of only radar data. In order todetermine whether a vehicle drives in front of the ego-vehicle aposition of the other vehicle is stored at a particular point in time.In succeeding time steps, the distance of the current position of theego-vehicle and the stored position of the other vehicle is evaluated.If the distance between the stored position of the other vehicle and theego-vehicle falls below a given threshold than the other vehicle isclassified to be on the same lane as the ego-vehicle, i.e. it is apredecessor on the ego-lane. Multiple instances of the method may run inparallel in order to accelerate detection of a change of thepredecessor.

The above-described approaches using only radar as the sensor inputeither require additional complex data input such as map data indicatinga number of lanes, or can only assign a car to be the predecessor in theego-lane. However, such input is not sufficient for many ADAS functionsand therefore has limited applicability only.

There is a need for a cost-efficient technique for lane assignment whichoperates reliably under a wide range of conditions such as fog, rain,darkness, and is of general applicability for various ADAS functions.

The above need is satisfied by a method of lane assignment in a vehicle.The method comprises the steps of providing one or more trajectories,wherein the or each trajectory represents a time sequence of positionsof a moving object; selecting one first object from the one or moremoving objects and the vehicle, wherein a first lane assignmentindicates a lane to which the first object is assigned; selecting onesecond object from the one or more moving objects; determining adistance between a current position of the first object and thetrajectory of the second object; comparing the distance with apredefined threshold; and providing, based on the first lane assignmentand a result of the comparison, a second lane assignment indicating alane to which the second object is assigned.

The vehicle (ego-vehicle) may be a car, truck, or bus, or in general anyobject intended for driving on a road, motorway, etc., which may includemanned vehicles driven by a driver but also automatically drivenvehicles such as robot vehicles. Similarly, the moving objects mayinclude any kind of vehicles, cars, trucks, busses, motor/cyclists, butalso trolleys, pedestrians, and even animals such as horses.

The trajectories of the moving objects may be updated according to ameasurement or sensing frequency (or less). Typically, an updatefrequency may be in the range of 1-10 Hertz. Lane assignments may beupdated with the same frequency or less. A trajectory may representmeasured or detected positions of a moving object for a time of, forexample, several seconds, e.g. 5 seconds.

Embodiments of the method may include the further step of acceptingobject data indicating one or more object positions as detected by asensor equipment such as a ranging system of the vehicle and/or providedby a Car2X environment. The ranging system may be a long-range systemsuch as an active or passive long-range system including, for example,at least one of a radar system and a lidar system.

In order to connect to a Car2X environment, the vehicle may comprisewireless communication equipment such as an antenna, a mobilecommunications receiver, etc.

The method may comprise the further step of evaluating if a detectedobject is a moving object or a static object. Such evaluation may bebased, for example, on velocity data included in the object-related dataprovided by the sensor equipment of the vehicle data.

Embodiments of the method may comprise the further step of calculatingone or more lane assignments based on image data of a camera. Some ofthese embodiments may comprise the further step of accepting dataindicating a road lane shape from a camera associated with the vehicle.

The step of determining the distance between the current position of thefirst object and the trajectory of the second object may comprisedetermining at least one of a lateral distance and an euclidian distancebetween the current position of the first object and the trajectory ofthe second object.

The predefined threshold may represent at least one of a lane width, forexample a width as defined according to statutory regulations, forexample for one or more different types of roads, and a vehicle width,for example an average vehicle width, e.g. an average width of a car ortruck. Corresponding data may be provided to a system for laneassignment on manufacture and/or during inspection.

According to embodiments of the method, the second lane assignment maybe provided to indicate, in case the determined distance is below thethreshold, the same lane as indicated by the first lane assignment.Additionally or alternatively, in case the determined distance is abovethe threshold, the method may comprise the further steps of determininga lateral position of the second object relative to the vehicle, andproviding the second lane assignment to indicate a lane to the left orright of the lane to which the vehicle is assigned.

Lane assignments may indicates a lane based on a position of the lanerelative to another lane. According to various embodiments, a relativelane assignment may be based on a lane assignment for the (ego-)vehicle.For example, a lane assignment may indicate a lane one or two positionsto the right or left of the ego-lane.

Embodiments of the method may comprise the further steps of determininga current forward distance, such as a depth or a z-distance, of thefirst object from the vehicle; and determining trajectories available ator for the determined current forward distance. Generally, a trajectorymay be determined to be an ‘available trajectory’ with respect to thecurrent position of the first object in case it is possible to determinea distance between the current position of the first object and thetrajectory. For example, in case the distance is calculated as anEuclidian distance in an (x, z) coordinate system, the trajectory maynot necessarily reach back to the current forward distance (z-value) ofthe first object, but the z-value of the end of the trajectory (theoldest position in the position history) may be larger than the z-valueof the first object.

The step of selecting the second object may then comprise selecting anobject associated with one of the determined available trajectories.Various embodiments may comprise the step of determining a relation listindicating objects with available trajectories for each of the one ormore moving objects and the vehicle.

According to various embodiments, a method according to the inventionmay comprise the further steps of building a relation graph representingrelations between pairs of moving objects, including determiningtrajectories which have to be considered for building the relationgraph, wherein a trajectory of a second object has to be considered if adistance between this trajectory and the trajectory of a first object isbelow a pre-determined threshold, and a relation between the first andthe second object is defined if the trajectory has to be considered; andpropagating lane assignments along the determined relation graph,including determining distances (d_(—)1, d_(—)2, d_(—)3) between pairsof moving objects having a relation with each other, and inferring laneassignments for the moving objects by assigning a lane to a secondobject of a pair of moving objects according to a lane assignment forthe first object of the pair of moving objects and the determineddistance for that pair of moving objects.

According to some of these embodiments, a trajectory has to beconsidered for building the relation graph if a line projectedperpendicular to the driving direction of a first object at a currentposition of a first object intersects with a trajectory of a secondobject.

The method may comprise the further steps of comparing multiple laneassignments for one and the same object; and selectively performing acorrection or conflict resolution process based on a result of thecomparison indicating conflicting lane assignments. The conflictresolution process may comprise the steps of providing a modifiedrelation list by removing one object from the object with theconflicting lane assignments and the objects related to the object withthe conflicting lane assignments from the relation list; andre-determining the lane assignments based on the modified relation list.According to some embodiments, a related object with the least number ofrelations may be removed from the relation list.

When looping through the list of detected moving objects including theego-vehicle for the list of first objects, the first and/or secondobjects may be selected in a depth-first-search or breath-first-search.

The method may include as a by-product the determination of number oflanes available in total or populated by the detected objects. The totalnumber of lanes may be indicated to a driver of the vehicle, for examplethe lanes of the road may be schematically indicated on a display.

The above-indicated need is further satisfied by a computer programproduct comprising program code portions for performing the methodaccording to any one of the methods and method aspects outlined above orelsewhere herein, when the computer program product is executed on acomputing device, for example one or more electronic processing modulesof a vehicle. The computer program product may be stored on a computerreadable recording medium, such as a permanent or re-writeable memorywithin or associated with a computing device or a removable CD-ROM, DVDor USB-stick. Additionally or alternatively, the computer programproduct may be provided for download to a computing device, for examplevia a data network such as the Internet or a communication line such asa telephone line or wireless link.

The above-indicated need is still further satisfied by a system forassisting a driver in a vehicle. The system may be equipped withsoftware, firmware, and/or hardware to perform a lane assignmentaccording to the invention. The system may comprise: a component adaptedto provide one or more trajectories, wherein the or each trajectoryrepresents a time sequence of positions of a moving object; a componentadapted to select one first object from the one or more moving objectsand the vehicle, wherein a first lane assignment indicates a lane towhich the first object is assigned; a component adapted to select onesecond object from the one or more moving objects; a component adaptedto determine a distance between a current position of the first objectand the trajectory of the second object; a component adapted to comparethe distance with a predefined threshold; and a component adapted toprovide, based on the first lane assignment and a result of thecomparison, a second lane assignment indicating a lane to which thesecond object is assigned.

The above-indicated need is also satisfied by a vehicle, which comprisesa system as outlined above or elsewhere herein.

According to one aspect, the invention may be interpreted as comprisinga technique for performing a lane assignment to a road user based onposition data only, wherein the position data is provided by along-range system such as radar or lidar, and/or by a Car2X environment.To this end, movement trajectories of at least one road user aredetermined, i.e. according to this aspect of the invention not onlycurrent position data are used, but also position data from the past(position history) are used, e.g. up to 5 seconds back. Thesetrajectories are then used for assigning road users to lanes. Forexample, for a given timepoint, current positions of a moving object arecompared to the trajectories of the other moving objects, if availableat the depth of the object under consideration (which may be theego-vehicle), and lane assignments may be performed according to one ormore of such comparisons.

The invention provides an approach for lane assignment based on onlyranging data such as provided by radar or lidar. A separate data sourcefor lane detection, such as provided by camera images, is notnecessarily required. Therefore, a lane assignment can be performedwithout a specific determination of road course or lane estimation.Instead, an estimation of the number of lanes can be provided as aby-product of the invention.

As a specific or separate road course estimation is dispensable, nocamera or similar imaging system is required and the invention can beapplied for vehicles without a camera or other imaging system.Accordingly, the inventive method is independent of weather conditionssuch as fog, rain, or darkness which deteriorate the results ofconventional methods of lane assignment. As the invention may rest onlyon data of a comparatively long-ranging system such as radar, theresults may be more reliable and may also reach longer in a forwardand/or backward direction of the ego-vehicle.

At the same time, the invention is generally applicable for many drivingassistant functions such as “Intelligent Adaptive Cruise Control”(IACC), as it provides lane assignments not only for one object movingin front of the ego-vehicle and/or for the ego-lane, but can providelane assignments for any detected moving object and multiple lanes tothe left and right of the ego-lane. For example an assignment to theleft or the right lane is important for lane change assistant systems.

While an inventive system can be employed without any input of a camera,in vehicles including for example an imaging system, an assistantfunction according to the invention can be provided as a substitute orcomplement to another lane assignment function, i.e. can be implementedas an add-on or a supplementary function, which is switched on toincrease, for example, the reliability of another lane assignmentfunction, for example in case of limited visibility conditions such asin fog, rain, snow or ice, or at night or in another situation when lanemarkings are not visible, for example in case of yellow instead of whitemarkings at a construction site, a polluted road or street, etc. Theinventive method can be switched on or added manually by the driverand/or automatically, e.g. in case a noise level of, e.g., an imagingsystem is detected to be above a predefined threshold.

Embodiments of the inventive method may comprise a step of comparing thenumber of detected moving objects with a given threshold number andselectively activating or de-activating the lane assignment procedure.For example for a given threshold of two, the inventive method may onlybe activated in case more than two moving objects are detected. In thisway the application of an embodiment of the inventive technique may belimited to traffic situations which allow reliable lane assignmentsand/or which require lane assignments at all.

Aspects of the invention comprise comparing the positions and/orposition histories of detected objects including the ego-vehicle notonly with the ego-vehicle, but with each other. For example, theinvention comprises the aspect of constructing a relation graphrepresenting relations between pairs of detected objects including theego-vehicle, wherein a first object is defined to be related to a secondobject if the trajectory of the second object is defined based on itsposition history at a depth (i.e. a forward or z-direction of theego-vehicle) of the current position of the first object. If this is thecase, lateral displacements of the first and second objects and, as aresult therefrom, at least relative lane assignments of the two objectscan be determined. Eventually, not only an ego-lane assignment relatedto a moving object directly ahead of the ego-vehicle can be determined,but general lane assignments can be determined. As a specific example,according to the invention multiple vehicles ahead or behind theego-vehicle can be assigned to the ego-lane, for example based on aradar system which can detect up to three vehicles in front of theego-vehicle.

In the following, the invention will further be described with referenceto exemplary embodiments illustrated in the figures, in which:

FIG. 1 schematically illustrates a first exemplary traffic situation ona road with ego-vehicle and further moving objects;

FIG. 2 illustrates functional components of an ECU of the ego-vehicle ofFIG. 1;

FIG. 3A is a flow diagram illustrating an operation of the ECU of FIG.2;

FIG. 3B is a continuation of the flow diagram of FIG. 3A;

FIG. 4A illustrates the traffic situation of FIG. 1 in an exemplarycoordinate system as employed by the ECU of FIG. 2;

FIG. 4B schematically illustrates an aspect of the invention based onthe situation of FIG. 4A;

FIG. 5A illustrates a relation graph according to the invention for asecond exemplary traffic situation;

FIG. 5B illustrates a relation graph according to the invention for athird exemplary traffic situation;

FIG. 6 illustrates a relation graph according to the invention for afourth exemplary traffic situation;

FIG. 7 illustrates relation graphs according to the invention for afifth exemplary traffic situation;

FIG. 8 illustrates four intermediate relation graphs according to theinvention for the fifth traffic situation; and

FIG. 9 illustrates a final relation graph with resolved conflictaccording to the invention for the fifth exemplary traffic situation.

FIG. 1 illustrates a traffic situation 100 on a road 102 which will beused as a reference for describing aspects of the invention below. Theroad 102 comprises two lanes 104, 106 in a direction of travel 108 ofego-vehicle 110, and may comprise further lanes 112, 114 which may bereserved for opposing traffic. The lanes are marked by continuous 116and broken 118 marking lines. Ahead of the ego-vehicle 110 there isfurther traffic represented by moving objects (in this case, furthercars or vehicles) 120, 122, 124. Further traffic may follow ego-vehicle110 but is omitted in FIG. 1 purely for reasons of conciseness. Opposingtraffic is exemplarily represented by a vehicle 126. One or more staticobjects 128 nearby to road 100 may comprise trees, walls or buildings,parking vehicles, road posts, etc.

The ego-vehicle 110 is equipped with an ECU (Electronic Control Unit)130, which may be realized in form of one or more hardware units onboardvehicle 110, onto which firmware and/or software is installed toimplement one or more driving assistants for a driver of vehicle 110. Itis assumed that at least one ADAS function is implemented which requiresa lane assignment function, i.e. a function that assigns the detectedmoving objects 120 and 124 to lane 104 (the ego-lane of vehicle 110),and assigns detected moving object 122 to lane 106 (the lane right tothe ego-lane).

FIG. 2 illustrates functional components of the ECU 130 of FIG. 1. Forsake of conciseness, only those functional blocks relevant forillustrating the invention are shown FIG. 2. An operation of the ECU 130will be described with reference to the flow diagram of FIGS. 3A, 3B.Generally, the operation 300 of the ECU 130 is described with respect togenerating or updating lane assignments for moving objects detected inthe environment of the vehicle 110.

In step 302, a trajectory component 204 of ECU 130 accepts object dataindicating one or more object positions as detected by the radar system202 of vehicle 110. While vehicle 110 may be equipped with varioussensor circuitry, only radar 202 is illustrated in FIG. 2 as providingdata to ECU 130, specifically to component 204 which is adapted toprovide trajectories of objects detected by radar system 202. Eachtrajectory may represent a time sequence of positions of a movingobject, i.e. the component 204 may operate to receive data at regularintervals from radar 202, for example with a frequency of 1 to 10 Hz.The component 204 may analyze the data to detect objects such as theobjects 120, 122, 124, and 128 in FIG. 1.

The component 204 may operate prior to generating or updatingtrajectories in order to discriminate static object 128 from movingobjects 120 to 124 based on, e.g., velocity data provided by radar 202.Generally, velocity data processed as being measured relative to theego-vehicle in case of ranging devices such as radar and as beingmeasured in a global coordinate system in case of a Car2X environment.

In step 304, the component 204 provides trajectories for the detectedmoving objects, which may include the component 204 generating,continuously updating and, e.g., cyclically overwriting a correspondingdata record for each moving object in an associated storage area 206.Such data record may comprise, for example, an identifier of thedetected moving object and a number of associated time stamps, whereineach time stamp in turn has data associated thereto which may compriseposition data, such as an x-coordinate and a z-coordinate in terms of anego-vehicle based coordinate system, a velocity and, if available, arelative or absolute lane identifier corresponding to earlier laneassignments. However, for sake of clarity lane assignments will bediscussed as being stored separately in the following.

The trajectory component 204 triggers 207 a relation component 208 at,for example, regular time intervals which may correspond to the timepoints the component 204 accepts data from radar 202. The trigger 207may also occur less frequent, for example in order to (temporarily)limit a computational burden of ECU 130. Component 208 is provided todetermine in a step a 306 a relation list (relation graph) as will bediscussed in detail below for indicating objects with availabletrajectories for each of the one or more moving objects 120, 122, 124and the vehicle 110, wherein the relation list is stored in a storagecomponent 210 also associated with ECU 130.

Before continuing with a detailed description of the operation of ECU130, the general role and purpose of the trajectories and the relationlist as provided at a particular point in time in storages 206, 210 willbe discussed with reference to high-level examples illustrated in FIGS.4A, 4B and 5.

FIG. 4A is a schematic illustration of the traffic situation of FIG. 1,wherein only the positions of the moving objects 120, 122, 124 shown asblack dots are given as detected by the radar of ego-vehicle 110 shownas a black triangle in a birds-eye-view, i.e. in an x-z plot withcoordinate z denoting a depth or forward-distance along a direction oftravel of vehicle 110 and coordinate x denoting a lateral coordinateextending perpendicular to the direction of travel, as known to theskilled person. The dashed line denotes the distance zero to theego-vehicle 110. It is apparent that based only on the positions of thethree detected moving objects, the three detected road users may hardlyassigned to one or more lanes. Generally, a reliable lane assignment mayhardly be performed based on scarce information only, such as positioninformation of only few objects.

FIG. 4B also refers to the exemplary traffic situation of FIG. 1,however, the traffic participants are tracked over time andcorresponding trajectories are generated for each detected object fromthe past measurements. Therefore, in addition to the positions of thedetected moving objects also their trajectories are illustrated asgenerated from the detected positions of these objects in previous timesteps.

A tracking of the detected objects over time may be accomplished using,e.g. object identifiers as assigned by the ranging device 202 of theego-vehicle 110 or the ECU 130 (e.g., the trajectory determinationcomponent 204). Therefore the detected objects are referenced in FIG.4B, instead of with reference numerals 120, 122, and 124, with exemplaryid-numbers id1, id2, and id3, respectively.

The trajectories of objects id1, id2 and id3 are shown as extending backto depth zero, i.e. the current position of the ego-vehicle 110;generally trajectories may end at positive z-values, e.g. in case ofnewly detected objects, or may end at negative z-values, i.e. behind theego-vehicle.

Generally, a trajectory may collect position data and/or other data of adetected object for a time range of several seconds up to about oneminute dependent on detection range, number of currentlydetected/tracked objects, relative and absolute velocities, etc. In atypical situation a trajectory may extend 5 seconds into the past.

Using position histories (i.e. trajectories) in addition to currentpositions of detected moving objects allows to apply a reasoning schemein order to reliably assign the different detected objects to differentlanes. For example, from the data illustrated in FIG. 4B it is clearthat the road users id1 and id2 are on the same lane as the ego-vehicle110 while the road user id3 travels on a lane to the right of theego-vehicle 110. This also implies the lane detection that there is infact a lane to the right of the ego-lane, which could not be inferredfrom only the current position data of FIG. 4A.

It is to be noted that the lane assignments and lane detection discussedabove can be inferred from the current position data and positionhistories as depicted in FIG. 4B without any additional road shape orlane information.

FIG. 5A will be referenced below to describe on a generic or high levelan exemplary technical implementation of what has been discussed withreference to FIG. 4B. FIG. 5A illustrates an instance of a radardetection (left portion of the figure) and how the inference for laneassignment may take place (relation graph depicted in the right portionof the figure). The traffic situation is no longer that of FIG. 1 forthe sake of discussion.

The interrelations between the ego-vehicle id:0 and the other detectedroad users with ids:410, 500, 502, and 506 and their trajectories spansa relation graph wherein the road users form the nodes and the linksconnect road users that are related to each other. A road user “A” isdefined as being related to another road user “B”, if the z-level of thecurrent position of user/object “A” crosses the trajectory of roaduser/object “B”. In other words, two road users are related to eachother if one object is at a depth the other object has already been inthe past, i.e. has already been detected in the past by, e.g., theranging system of the ego-vehicle. As an alternative to determining apurely lateral distance or x-distance, any other (minimum) distanceprescription between the current position of an object and a trajectoryof another object can be computed, e.g. an Euclidean distance.

Referring to the specific example situation depicted in FIG. 5A, theroad user with id:410 is currently at a depth of 25 meters (m).Therefore, road user 410 is related to the road users 500 and 502because the trajectories of id:500 and id:502 reach back to the depth of25 m. In contrast, road user with id:506 is not related to user 410because the trajectory of detected moving object 506 reaches back onlyto a depth of 45 m. These relationships are represented or mirrored inthe graph structure: There is a link from node 410 to node 500 and node502, but no link from node 410 to node 506.

Once the graph as shown in the left portion of FIG. 5A is set up, theinference to achieve lane assignments can be done. Assigned lanes aredepicted in the boxes at the top right of each node in the relationgraph of FIG. 5A. The inference process may start with the ego-vehicleid:0, but any other starting point may also be used. First, theego-vehicle 0 is assigned arbitrarily to lane 0. Then, for all links orrelations in the graph, the x-distance (alternatively another distancemeasure such as an Euclidean distance) between the two related or linkedobjects or road users is determined, i.e. the distance between thecurrent position of one road user and the trajectory of the other roaduser. These distances are indicated with double-arrows in the radar plotof FIG. 5A (left portion).

If the distance determined as before is below a specific threshold, thetwo related road users are supposed to be on one and the same lane. Forexample, the trajectory of road user 500 has a small x-distance only tothe current position of the ego-vehicle 0 at z=0. Therefore it isdecided that road user with id:500 is on the same lane as theego-vehicle 0, i.e. both are assigned to lane 0 (see right portion ofFIG. 5A). The trajectory of road user 410, on the other hand, has alarge x-distance to the ego-vehicle 0 at the current position of theego-vehicle at z=0, and it is decided that object 410 is on anotherlane. Specifically, road user 410 is located to the right of theego-vehicle 0. Hence, object 410 is assigned to the lane right to lane0, i.e. lane 1 (in case of a prescription defining ego-lane=0, laneright to ego-lane=1, lane left to ego-lane=−1, etc.).

The inference as outlined above may scan all nodes and relations of therelation graph until all nodes (road users, detected moving objects)have been assigned a lane label (lane identifier or lane index).Specifically, such scan may follow a scheme such as depth-first-searchor breath-first-search.

FIG. 5B depicts another exemplary scenario to more specificallyillustrate a determination and usage of the relations between the movingobjects for inferring lane assignments.

In an (x,z) coordinate system of the ego-vehicle ID:0, dashed lines 542,544, 546, 548 indicate forward distances for the ego-vehicle (assumed tobe at z=0) and detected moving objects with IDs 502, 600, and 500,respectively. Trajectories 552, 554, and 556 of objects 502, 600 and500, respectively, are assumed to be represented in the assistancesystem of the ego-vehicle. A relation graph representing relationsbetween the ego-vehicle and the detected objects is indicated witharrows 562, 564, 566.

For determining the relation graph, a loop is performed over all objectsincluding the ego-vehicle. For each object under consideration, it isdetermined which of the trajectories stored in the system are relevant.A trajectory may be considered relevant if it is available at thecurrent forward distance of the object under consideration (for theego-vehicle, the current forward distance is z=0). In terms of theillustration in FIG. 5B, a trajectory may be considered relevant if itintersects with the horizontal dashed line (constant z value) associatedwith the object under consideration. If a particular trajectoryintersects, the object under consideration currently passes a point(more precisely, a line perpendicular to the driving direction) wherethe moving object, to which the trajectory is assigned to, has been inthe past.

Referring to the specific example depicted in FIG. 5B, the ego-vehicleID:0 is currently at a point where ID:502 has been in the past, becausethe z-line 542 of ID:0 intersects with the trajectory 552 of object 502.However, the ego-vehicle ID:0 has no intersection with either ID:500 orID:600. Therefore, the ego-vehicle ID:0 is represented in the relationgraph as having a relation 562 with ID:502, but no further relations tothe other two objects is stored. Further, ID:502 has a relation 564 toID:600 and ID:600 has a relation 566 to ID:500.

After the relation graph has been determined, a lane assignmentprocedure may be propagated along the relations represented in thegraph. Specifically, as there is a relation established between ID:0 andID:502, a distance d_(—)1 is determined. As the distance d_(—)1 isdetermined to be above a threshold (e.g., a lane width), it is concludedthat a lane identifier ‘1’ is to be assigned to ID:502, which is anassignment performed relative to an initial (arbitrary) lane assignmentof identifier ‘0’ (ego-lane) to the ego-vehicle. No distancedetermination is attempted for ID:500 or ID:600, as no relation of theego-vehicle to one or both of these objects is represented in the graph.By propagating further along the relation graph, based on determiningthe distance d_(—)2, a lane identifier ‘−1’ is assigned to ID:600, andbased on determining the distance d_(—)3, a lane identifier ‘0’ isassigned to ID:500.

The simple example of FIG. 5B illustrates a specific method of achievinga consistent lane assignment pattern to the detected moving objects.Based on the relations as represented in the relation graph, apropagation path is followed along the graph, starting from theego-vehicle with an arbitrary initial lane assignment. Along thepropagation path, also the lane assignments are propagated, i.e. a laneassignment of a first object under consideration is propagated to asecond object having an intersecting trajectory with the z-distance ofthe first object, wherein the lane assignment of the second object isthe lane assignment of the first object updated by the result of thedistance determination between the first object and the intersectingtrajectory.

Referring again to FIGS. 2 and 3 and returning thereby to the specificfunctional blocks of ECU 130 of vehicle 110 and the operation thereof,relation determining component 208 may operate to determine a relationlist similar to that shown in FIG. 5, right portion, for the currenttraffic situation as depicted in FIG. 1, i.e. component 208 determinesrelations between objects 120, 122, 124 and the ego-vehicle 110 andstores the generated or updated current relation list in storage 210.

Then, component 208 triggers a functional loop component 212 which isprovided to work along the nodes and links of the stored relation graphaccording to a prescribed algorithm, for example according to adepth-first-search or breath-first-search scheme. As such schemes areknown as such to the person of skill, further details are omitted inthis respect. However, with reference to some specific aspects of thescan performed by component 212, functional components 214 and 216 areillustrated in FIG. 2.

Component 214 is adapted to select in step 308 one first object from theone or more detected moving objects 120, 122 and 124 as represented instorages 206 and 210 and the ego-vehicle 110. Component 216 is adaptedto select in step 310 a second object from the one or more movingobjects represented in components 206 and 210, wherein the second objectis different from the first object currently selected by component 214.

Functional calculation components 220 and 222 are provided, whereindistance component 220 is adapted to determine in step 312 a distancebetween a current position of the first object currently selected bycomponent 214 and the trajectory of the second object currently selectedby component 216. Discrimination component 222 is amongst others adaptedto compare in step 314 the distance determined by component 220 with apredefined threshold stored in a further storage component 224 which isassociated with ECU 130 and may provide various fixed, permanent and/orsemipermanent data provided at a time of manufacture of the vehicle 110and/or updated by service personnel during inspection.

Lane assignments indicating a lane to which one of the stored objects orthe ego-vehicle is assigned are illustrated as being stored in aseparate storage component 218 in FIG. 2 for reasons of discussion. Inpractice such lane assignments may be stored in association to thetrajectory data in storage 206 (which may also be structured accordingto objects or identifiers) and/or the relation graph data in storage210; in one implementation, the relation lists of storage 210 may alsobe stored in conjunction with the data records and the lane assignmentsstored in storage 206. In any case, i.e. also in case of extra storageas in the embodiment of FIG. 2, the lane assignments require storage inassociation to the ids of the detected objects and the ego-vehicle.

Based on results provided by loop component 212, an assignment component226 is adapted to provide (step 316), i.e. generate or update, laneassignments, wherein each assignment comprises an association (datapair) of an object id and a lane label. To this end, the component 226accesses assignment storage 218 and provides a new or updated laneassignment for the currently selected second object relative to anexisting lane assignment of the currently selected first object asretrieved from storage 218. For example, in case the determined distanceis below the threshold, the same lane as indicated by the retrieved laneassignment is assigned to the second object. In case the determineddistance is above the threshold, a lateral position of the currentlyselected second object is determined relative to the ego-vehicle. Thenew or updated lane assignment then indicates a lane to the left orright of the lane to which the ego-vehicle is assigned.

Generally, a relative lane assignment procedure as described here maystart with assigning a predefined lane label to the lane of theego-vehicle, e.g. the lane assignment of the ego-vehicle may beprescribed as a starting point or permanently as assigning theego-vehicle with id “0” to lane label “0”.

The component 226 may store the new or updated lane assignment instorage 218 and may return control to the loop component 212. In casefurther second objects have to be scanned (decision 318), the component216 is activated; in case further first objects are to be scanned(decision 320), the component 214 is activated. Generally, the firstobjects to be scanned comprise all nodes in the relation graph currentlystored in storage 210 including the ego-vehicle. For each first object,the second objects to be scanned or looped through comprise the detectedobjects related to the first object as represented in the relation graphin storage 210.

In case the scan over the relation graph stored in storage 210 has beenfinished, control is handed over to a conflict resolving component 228.Before describing in detail the operation thereof, FIGS. 6 to 9 arereferred to for a high-level discussion of corrections which may have tobe applied to a set of lane assignments as generated by a single, firstscan over a relation graph.

It is to be understood that generally multiple lane assignments aregenerated for one and the same detected moving object, i.e. object id,because the object in general can be related to multiple other objects.As an example, in FIG. 5, object 410 is related to objects 500 and 502,as their trajectories cross the current depth of object 410.Consequently, two lane assignments will be generated, both consistentlyassigning a lane right to the lane of objects 500 and 502 to object 410.

However, inconsistencies can occur in the lane assignments, for examplebecause a road user may perform a lane change during a time span atleast partly covered by the trajectory of that object maintained in theego-vehicle. In case of a completed lane change, the trajectory maytouch two (or even more) lanes. In case of an ongoing lane change or alane change partly represented in the trajectory, no consistentassignment of the lane-changing object to a lane may be found.

FIG. 6 illustrates an exemplary situation in this respect, wherein thepresentation is similar to FIG. 5 in that the left portion of FIG. 6represents a radar plot with positions and position histories(trajectories) for various detected objects, while the right portionillustrates a corresponding relation graph as may be generated from thesituation depicted in the left portion.

FIG. 6 concerns a situation wherein road user 502 changes from a leftlane (e.g., lane 0, i.e. the ego-lane of the ego-vehicle 0) to a rightlane (e.g., lane 1, i.e. the lane to the right of lane 0). Specifically,the trajectory of id 502 is assigned to lane 0 in the past and may beassigned to lane 1 at the current time point, as the object 502 is aboutto change to lane 1.

As a result, lane assignments for object 502 based on its relations toobjects 410 and 500 assign lane 0 to object 502, while a lane assignmentfor object 502 based on its relation to object 506 assigns lane 1 toobject 502. The reason for the conflicting assignments is that road user502 is currently about to be on the same lane as road user 506, but wasleft of road user 410 in the past, while road users 410 and 506 arecurrently on the same lane. The resulting conflicting assignments areindicated in the relation graph in the right portion of FIG. 6 forobject 502.

Various approaches can be contemplated to resolve inconsistencies orconflicts such as that depicted in FIG. 6. The embodiment which will bediscussed in the following comprises of two steps: First, all nodes thatmay be responsible for the conflict are identified. Second, anappropriate conflict handling is applied, as described in the following.

While conflicting assignments often result from a lane change, this doesnot necessarily imply that the lane change is or has been performed bythe node (object) with the conflicting assignments. In fact, besides theconflicting node itself, one or more of all those nodes which arerelated to the conflicting node, i.e. for which there is a path to theconflicting node, can be responsible for the conflict.

FIG. 7 depicts in its left portion an example situation which is morecomplex than that of FIG. 6, and which will be referred to for reasonsof discussion. It is assumed that conflicting lane assignments aredetected for node 602. The right portion of FIG. 7 depicts those nodesof the relation graph with dashed lines that cannot be the reason forthe conflict, because there is no path from these nodes to node 602.This is true for the nodes 598 and 599.

As the other nodes 0, 601, 609, 610 and 602 are related directly orindirectly to the conflicting node 602, one or more of these nodes mighthave caused the conflict.

In case node 0 (ego-vehicle) is used as a basis for relative laneassignment, node 0 cannot be the reason for the conflict. However, node0 would have to be considered, for example, in implementations in whichtrajectories may extend behind the current position of the ego-vehicle,i.e. if trajectories are prolonged into the negative z region below theego-vehicle in x-z plots similar to those of the preceding figuresherein.

In order to resolve the conflict and to identify the node which isresponsible for the conflict in the relation graph of FIG. 7, for eachpotential conflict node a new graph may be generated, in which thepotential conflict node is removed.

FIG. 8 shows the graphs resulting from removing a respective one of thepotential conflict nodes 602, 601, 609, and 610 from the example graphof FIG. 7.

According to the top right graph in FIG. 8, when removing node 601 fromthe relation graph, the conflict continues to be present. As theconflict remains when removing node 601, it is to be decided that nodeor object 601 cannot be the reason for the conflict.

The further graphs of FIG. 8 show that removing any one of nodes 609,610 and 602 results in the conflict disappearing, i.e. one of thesenodes is the reason for the conflict.

At this point, either additional information or a reasonable assumptionis required as input for the system to identify the node or objectresponsible for the conflict. Information may be provided by, e.g., by alane detection device such as a camera. As another approach, additionalinformation can be gathered from a ranging system, preferably the systemwhich provides data on detected objects. For example, additional laneshape or road course information may be extracted from objects which aredetected by a radar system and classified as static and dynamic (moving)objects.

One or more suitable assumptions may additionally or alternatively beprovided to the system. As but one example, from among multiple nodespotentially responsible for a conflict, the node having the least numberof connections (relations) in the graph may be selected as theconflict-resolving node. The basis for the approach can be seen in thatthe least information about its relative position is available for theselected node, i.e. the probability for an error in relative laneassignment is highest for that node.

In the example of FIGS. 7 and 8, node 609 has four relations, node 602has three relations, and node 610 has two relations. Therefore followingthe above assumption node 610 may be removed from the relation graph.The graph as shown in the lower right of FIG. 8 will therefore form thebasis for the further determination.

FIG. 9 illustrates the result of the conflict resolving procedure: Whilenode 610 has been removed from the graph and will be left without anylane assignment, the lane assignments for the other nodes or objects aredetermined without reference to node 610, and therefore a consistent setof lane assignments can be determined for all nodes except node 610.

Other or further assumptions for resolving inconsistencies or conflictsin the lane assignments may be implemented or applied, for example,dependent on the driving assistant function relying upon the laneassignments. As an example, in situations in which lane changes can beexpected to happen rarely, the system may decide to simply wait for theconflict to resolve and disappear within the next few seconds. Suchsimple approach may be desired sufficient for comfort functions such aslane change assistants which are not time-critical.

As another example, from among multiple nodes potentially responsiblefor a conflict, the node or nodes being nearest in depth (e.g.,z-distance, forward distance) to the conflicting node may be kept in thegraph while removing the potential conflict-causing node having thelargest z-distance to the conflicting node from the graph. The rationaleis that decisions may be decided less reliable when based on relationsassumed over a longer timescale and more reliable when based onshort-term relations.

Different assumptions may be applied for conflict resolution and/ormultiple assumptions may be applied to arrive at a reliable decision onconflicting lane assignments. In case of multiple conflicts one or moreof the above-described procedures may be applied multiple times untilany conflict is removed from a relation graph. While the nodes orobjects removed from the graph will have no lane assignments, allremaining nodes will have consistent lane assignments.

Referring back to ECU 130 in FIG. 2, the conflict resolving component228 operates in step 322 to detect and resolve one or more conflicts inthe current lane assignments as stored in storage 218. To this end,component 228 is adapted to compare multiple lane assignments availablein storage 218 for one and the same object identifier. Based on a resultof the comparison, in case of a conflicting lane assignment thecomponent performs a conflict resolution process as has been discussedexemplarily above.

Process 300 ends (324) as soon as a set of consistent lane assignmentsis available for a maximum of the detected moving objects. Thecorresponding data may be made available to other components of the ECU130 or vehicle 110 as indicated by arrow 230 in FIG. 2. For example, thedata including the lane assignments and the total number of detectedlanes may be indicated to one or more ADAS functions, and/or may beprovided for an output on, e.g., a display of the ego-vehicle. The datamay also be provided to other moving objects nearby based on, e.g., aCar2X environment, which may contribute to generating reliableprocessing results.

While in the examples above lane assignments have been performed formoving objects detected in front of the ego-vehicle, i.e. in a forwarddirection (direction of travel) of the ego-vehicle, embodiments of theinvention are also applicable for additionally or alternatively assignlanes to traffic behind the ego-vehicle, i.e. in a backward direction(direction opposite to the direction of travel) of the ego-vehicle. Forexample, trajectories (position histories) can be continuously tracedeven if the ego-vehicle passes by earlier positions of detected movingobjects.

Trajectories may even be set up entirely behind the ego-vehicle.Distances between current positions and trajectories can also bedetermined in a backward direction of the ego-vehicle. Correspondinglane assignments may be of value for functions such as lane changeassistants, braking assistants, cruise control, etc.

As the inventive technique does not depend on lane markings, laneassignments can also be performed in areas of fuzzy markings or wheremarkings are obscured, e.g. in road construction areas, or when markingsare obscured by snow or ice.

While the invention has been described in relation to its preferredembodiments, it is to be understood that this description is intendednon-limiting and for illustrative purposes only. In particular, variouscombinations of features which have been described separatelyhereinbefore are apparent as advantageous or appropriate to the skilledartisan. Accordingly, it is intended that the invention be limited onlyby the scope of the claims appended hereto.

The invention claimed is:
 1. A method of lane assignment in a vehicle,comprising the steps of: providing, by an electronic control unit of thevehicle, one or more trajectories, wherein each trajectory represents atime sequence of positions of a moving object; selecting, by theelectronic control unit of the vehicle, one first object from the one ormore moving objects and the vehicle, wherein a first lane assignmentindicates a lane to which the first object is assigned; selecting, bythe electronic control unit of the vehicle, one second object from theone or more moving objects; determining, by the electronic control unitof the vehicle, a distance between a current position of the firstobject and the trajectory of the second object; comparing, by theelectronic control unit of the vehicle, the distance with a predefinedthreshold; providing, based on the first lane assignment and a result ofthe comparison, and by the electronic control unit of the vehicle, asecond lane assignment indicating a lane to which the second object isassigned; building, by the electronic control unit of the vehicle, arelation graph representing relations between pairs of moving objects,including determining trajectories which have to be considered forbuilding the relation graph, wherein a trajectory of a second object hasto be considered if a distance between this trajectory and thetrajectory of a first object is below a pre-determined threshold, and arelation between the first and the second object is defined if thetrajectory has to be considered; and propagating, by the electroniccontrol unit of the vehicle, lane assignments along the determinedrelation graph, including determining distances (d-1, d-2, d-3) betweenpairs of moving objects having a relation with each other, and inferringlane assignments for the moving objects by assigning a lane to a secondobject of a pair of moving objects according to a lane assignment forthe first object of the pair of moving objects and the determineddistance for that pair of moving objects.
 2. The method according toclaim 1, comprising the further step of accepting object data indicatingone or more object positions as detected by a ranging system of thevehicle and/or provided by a Car2X environment.
 3. The method accordingto claim 1, wherein the step of determining the distance comprisesdetermining at least one of a lateral distance and an Euclidian distancebetween a current position of the first object and the trajectory of thesecond object.
 4. The method according to claim 1, wherein thepredefined threshold represents at least one of a lane width and avehicle width.
 5. The method according to claim 1, wherein the secondlane assignment is provided to indicate, in case the determined distanceis below the threshold, the same lane as indicated by the first laneassignment.
 6. The method according to claim 1, comprising, in case thedetermined distance is above the threshold, the further steps ofdetermining a lateral position of the second object relative to thevehicle, and providing the second lane assignment to indicate a lane tothe left or right of the lane to which the vehicle is assigned.
 7. Themethod according to claim 1, wherein a lane assignment indicates a lanebased on a position of the lane relative to another lane.
 8. The methodaccording to claim 1, comprising the further steps of determining acurrent forward distance of the first object from the vehicle; anddetermining trajectories available at the determined current forwarddistance; wherein the step of selecting the second object comprisesselecting an object associated with one of the determined availabletrajectories.
 9. The method according to claim 1, wherein a trajectoryhas to be considered for building the relation graph if a line projectedperpendicular to the driving direction of a first object at a currentposition of a first object intersects with a trajectory of a secondobject.
 10. The method according to claim 1, comprising the furthersteps of comparing multiple lane assignments for one and the sameobject; and selectively performing a conflict resolution process basedon a result of the comparison indicating conflicting lane assignments.11. The method according to claim 10, wherein the step of selecting onesecond object from the one or more moving objects comprises determininga relation list indicating objects with available trajectories for eachof the one or more moving objects and the vehicle; and wherein theconflict resolution process comprises the steps of: providing a modifiedrelation list by removing one object from the object with theconflicting lane assignments and the objects related to the object withthe conflicting lane assignments from the relation list; andre-determining the lane assignments based on the modified relation list.12. The method according to claim 11, wherein a related object with theleast number of relations is removed from the relation list.
 13. Themethod according to claim 1, wherein first and/or second objects areselected in a depth-first-search or breath-first-search.
 14. A computerprogram, embodied on a non-transitory computer readable medium, thecomputer program, when executed by a processor, causes the processor to:provide one or more trajectories, wherein each trajectory represents atime sequence of positions of a moving object; select one first objectfrom the one or more moving objects and the vehicle, wherein a firstlane assignment indicates a lane to which the first object is assigned;select one second object from the one or more moving objects; determinea distance between a current position of the first object and thetrajectory of the second object; compare the distance with a predefinedthreshold; provide, based on the first lane assignment and a result ofthe comparison, a second lane assignment indicating a lane to which thesecond object is assigned; build a relation graph representing relationsbetween pairs of moving objects, including determining trajectorieswhich have to be considered for building the relation graph, wherein atrajectory of a second object has to be considered if a distance betweenthis trajectory and the trajectory of a first object is below apre-determined threshold, and a relation between the first and thesecond object is defined if the trajectory has to be considered; andpropagate lane assignments along the determined relation graph,including determining distances (d-1, d-2, d-3) between pairs of movingobjects having a relation with each other, and inferring laneassignments for the moving objects by assigning a lane to a secondobject of a pair of moving objects according to a lane assignment forthe first object of the pair of moving objects and the determineddistance for that pair of moving objects.
 15. A system for assisting adriver in a vehicle, comprising: a radar system; and an electroniccontrol unit, wherein the electronic control unit is configured toprovide one or more trajectories, wherein each trajectory represents atime sequence of positions of a moving object; select one first objectfrom the one or more moving objects and the vehicle, wherein a firstlane assignment indicates a lane to which the first object is assigned;select one second object from the one or more moving objects; determinea distance between a current position of the first object and thetrajectory of the second object; compare the distance with a predefinedthreshold; provide, based on the first lane assignment and a result ofthe comparison, a second lane assignment indicating a lane to which thesecond object is assigned; build a relation graph representing relationsbetween pairs of moving objects by determining trajectories which haveto be considered for building the relation graph, wherein a trajectoryof a second object has to be considered if a distance between thistrajectory and the trajectory of a first object is below apre-determined threshold, and a relation between the first and thesecond object is defined if the trajectory has to be considered; andpropagate lane assignments along the determined relation graph bydetermining distances (d-1, d-2, d-3) between pairs of moving objectshaving a relation with each other, and inferring lane assignments forthe moving objects by assigning a lane to a second object of a pair ofmoving objects according to a lane assignment for the first object ofthe pair of moving objects and the determined distance for that pair ofmoving objects.
 16. A vehicle, comprising: an electronic control unitconfigured to provide one or more trajectories, wherein each trajectoryrepresents a time sequence of positions of a moving object; select onefirst object from the one or more moving objects and the vehicle,wherein a first lane assignment indicates a lane to which the firstobject is assigned; select one second object from the one or more movingobjects; determine a distance between a current position of the firstobject and the trajectory of the second object; compare the distancewith a predefined threshold; provide, based on the first lane assignmentand a result of the comparison, a second lane assignment indicating alane to which the second object is assigned; build a relation graphrepresenting relations between pairs of moving objects by determiningtrajectories which have to be considered for building the relationgraph, wherein a trajectory of a second object has to be considered if adistance between this trajectory and the trajectory of a first object isbelow a pre-determined threshold, and a relation between the first andthe second object is defined if the trajectory has to be considered; andpropagate lane assignments along the determined relation graph bydetermining distances (d-1, d-2, d-3) between pairs of moving objectshaving a relation with each other, and inferring lane assignments forthe moving objects by assigning a lane to a second object of a pair ofmoving objects according to a lane assignment for the first object ofthe pair of moving objects and the determined distance for that pair ofmoving objects.